The semantics of words and phrases in any language can be digitized in the form of real-valued vectors, i.e. bounded-dimensional vectors with only a finite number of nonzero entries. Such representations of words and phrases are called “semantic vectors” and the collection of all such vectors is called a “semantic space.”
Researchers and practitioners have used such representations in information retrieval, semantic similarity analysis, scoring of text quality, and analyses of discourse cohesion. Over the past 20 years, needs have grown to the point where a systematic method is necessary to evaluate and optimize semantic spaces.